Overview:

This thesis presents
AMBR3
-- a dynamic emergent integrated model of analogical access and mapping based on decentralized representations of situations. It describes in detail the knowledge structures and computational mechanisms used in the model. The behavior of the model is illustrated by many examples, diagrams, and transcripts of actual runs of the computer implementation of the model. The thesis reports the results of various simulation experiments involving more than 1200 runs of the program on different target problems. AMBR is compared with a selection of other models and is discussed in the light of the studies of human analogy-making.

AMBR
is an emergent and decentralized model. It consists of a population of small entities called DUAL agents. These agents are the ingredients of the
DUAL cognitive architecture
that is the foundation of AMBR. They represent all the knowledge and carry out all the processing in the architecture. There is no central executive that controls the operation of the system as a whole. Instead, each agent works locally and performs its simple specific task in close interaction with its immediate neighbors. The global behavior of the model emerges of the coordinated effort of these asynchronous local activities.

AMBR
applies the same approach to the phenomena it is intended to model. The subprocesses of analogy-making are explained in terms of coordinated mechanisms. The main intuition behind the research reported here is that there is no 'analogy machine' that does analogies according to some fixed centralized algorithm. Instead, analogy is an emergent product of the work of general cognitive mechanisms. The thesis tries to demonstrate that such approach is feasible. Thus, analog access is based on the mechanism of spreading activation which serves a range of other purposes in the cognitive architecture. The constraint satisfaction mechanism is used for finding correspondences in the model but the same mechanism can apply to various other tasks such as perception and decision making.

AMBR
representations of episodes are decentralized. The model does not maintain data structures listing the elements that belong to each situation. Instead, each situation is represented by a coalition of agents.
This allows for greater flexibility of the representations. New elements
can be added when necessary. The skolemization mechanism can augment the description of a given episode based on general semantic information. In the same time, elements that have been needed in the past and potentially belong to the description of the episode stay out of the working memory when they are irrelevant for the problem being solved. Thus the model is capable to re-represent a situation both by addition and omission of elements. Chapter V demonstrates this on a concrete example.

The theme of integration is central for AMBR research. The model conceptualizes the components of analogy-making not as sequential 'stages' but as subprocesses that run in parallel and interact. The version reported in this thesis integrates the subprocesses of analog access and mapping.
A case study reported in Chapter VI illustrates an interaction of this kind. Other simulation experiments from the same chapter also demonstrate various aspects of these interactions. Chapter VII suggests possibilities for modeling the subprocesses of transfer and perception. It is argued that they could be added to the current version of the model without forcing radical reconsideration of the existing mechanisms.

The computational dynamics is a characteristic feature of the architecture DUAL and, consequently, of the model built on top of it. Each DUAL agent works at its own speed that varies dynamically as the activation level of the agent vary. Thus, more relevant agents work faster and contribute more to the overall behavior of the system than do less relevant (and hence less active) ones. In addition, the topology of the AMBR network is constantly changing as new nodes and links are created while others are removed.
This dynamic emergent computation provides for flexibility and
efficiency at the same time.